mirror of
https://github.com/sd-webui/stable-diffusion-webui.git
synced 2024-12-14 23:02:00 +03:00
299 lines
8.5 KiB
Python
299 lines
8.5 KiB
Python
from inspect import isfunction
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import math
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import torch
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import torch.nn.functional as F
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from torch import nn, einsum
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from einops import rearrange
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from ldm.modules.diffusionmodules.util import checkpoint
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def exists(val):
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return val is not None
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def uniq(arr):
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return {el: True for el in arr}.keys()
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def default(val, d):
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if exists(val):
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return val
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return d() if isfunction(d) else d
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def max_neg_value(t):
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return -torch.finfo(t.dtype).max
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def init_(tensor):
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dim = tensor.shape[-1]
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std = 1 / math.sqrt(dim)
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tensor.uniform_(-std, std)
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return tensor
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# feedforward
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class GEGLU(nn.Module):
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def __init__(self, dim_in, dim_out):
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super().__init__()
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self.proj = nn.Linear(dim_in, dim_out * 2)
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def forward(self, x):
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x, gate = self.proj(x).chunk(2, dim=-1)
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return x * F.gelu(gate)
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class FeedForward(nn.Module):
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def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0):
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super().__init__()
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inner_dim = int(dim * mult)
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dim_out = default(dim_out, dim)
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project_in = (
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nn.Sequential(nn.Linear(dim, inner_dim), nn.GELU())
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if not glu
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else GEGLU(dim, inner_dim)
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)
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self.net = nn.Sequential(
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project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out)
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)
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def forward(self, x):
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return self.net(x)
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def zero_module(module):
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"""
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Zero out the parameters of a module and return it.
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"""
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for p in module.parameters():
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p.detach().zero_()
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return module
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def Normalize(in_channels):
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return torch.nn.GroupNorm(
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num_groups=32, num_channels=in_channels, eps=1e-6, affine=True
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)
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class LinearAttention(nn.Module):
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def __init__(self, dim, heads=4, dim_head=32):
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super().__init__()
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self.heads = heads
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hidden_dim = dim_head * heads
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self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias=False)
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self.to_out = nn.Conv2d(hidden_dim, dim, 1)
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def forward(self, x):
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b, c, h, w = x.shape
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qkv = self.to_qkv(x)
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q, k, v = rearrange(
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qkv, "b (qkv heads c) h w -> qkv b heads c (h w)", heads=self.heads, qkv=3
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)
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k = k.softmax(dim=-1)
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context = torch.einsum("bhdn,bhen->bhde", k, v)
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out = torch.einsum("bhde,bhdn->bhen", context, q)
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out = rearrange(
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out, "b heads c (h w) -> b (heads c) h w", heads=self.heads, h=h, w=w
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)
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return self.to_out(out)
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class SpatialSelfAttention(nn.Module):
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def __init__(self, in_channels):
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super().__init__()
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self.in_channels = in_channels
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self.norm = Normalize(in_channels)
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self.q = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0
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)
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self.k = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0
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)
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self.v = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0
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)
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self.proj_out = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0
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)
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def forward(self, x):
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h_ = x
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h_ = self.norm(h_)
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q = self.q(h_)
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k = self.k(h_)
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v = self.v(h_)
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# compute attention
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b, c, h, w = q.shape
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q = rearrange(q, "b c h w -> b (h w) c")
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k = rearrange(k, "b c h w -> b c (h w)")
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w_ = torch.einsum("bij,bjk->bik", q, k)
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w_ = w_ * (int(c) ** (-0.5))
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w_ = torch.nn.functional.softmax(w_, dim=2)
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# attend to values
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v = rearrange(v, "b c h w -> b c (h w)")
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w_ = rearrange(w_, "b i j -> b j i")
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h_ = torch.einsum("bij,bjk->bik", v, w_)
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h_ = rearrange(h_, "b c (h w) -> b c h w", h=h)
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h_ = self.proj_out(h_)
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return x + h_
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class CrossAttention(nn.Module):
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def __init__(
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self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0, att_step=1
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):
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super().__init__()
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inner_dim = dim_head * heads
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context_dim = default(context_dim, query_dim)
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self.scale = dim_head**-0.5
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self.heads = heads
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self.att_step = att_step
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self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
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self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
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self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
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self.to_out = nn.Sequential(
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nn.Linear(inner_dim, query_dim), nn.Dropout(dropout)
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)
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def forward(self, x, context=None, mask=None):
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h = self.heads
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q = self.to_q(x)
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context = default(context, x)
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k = self.to_k(context)
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v = self.to_v(context)
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del context, x
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q, k, v = map(lambda t: rearrange(t, "b n (h d) -> (b h) n d", h=h), (q, k, v))
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limit = k.shape[0]
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att_step = self.att_step
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q_chunks = list(torch.tensor_split(q, limit // att_step, dim=0))
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k_chunks = list(torch.tensor_split(k, limit // att_step, dim=0))
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v_chunks = list(torch.tensor_split(v, limit // att_step, dim=0))
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q_chunks.reverse()
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k_chunks.reverse()
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v_chunks.reverse()
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sim = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device)
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del k, q, v
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for i in range(0, limit, att_step):
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q_buffer = q_chunks.pop()
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k_buffer = k_chunks.pop()
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v_buffer = v_chunks.pop()
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sim_buffer = (
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einsum("b i d, b j d -> b i j", q_buffer, k_buffer) * self.scale
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)
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del k_buffer, q_buffer
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# attention, what we cannot get enough of, by chunks
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sim_buffer = sim_buffer.softmax(dim=-1)
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sim_buffer = einsum("b i j, b j d -> b i d", sim_buffer, v_buffer)
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del v_buffer
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sim[i : i + att_step, :, :] = sim_buffer
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del sim_buffer
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sim = rearrange(sim, "(b h) n d -> b n (h d)", h=h)
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return self.to_out(sim)
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class BasicTransformerBlock(nn.Module):
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def __init__(
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self,
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dim,
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n_heads,
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d_head,
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dropout=0.0,
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context_dim=None,
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gated_ff=True,
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checkpoint=True,
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):
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super().__init__()
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self.attn1 = CrossAttention(
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query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout
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) # is a self-attention
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self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
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self.attn2 = CrossAttention(
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query_dim=dim,
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context_dim=context_dim,
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heads=n_heads,
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dim_head=d_head,
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dropout=dropout,
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) # is self-attn if context is none
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self.norm1 = nn.LayerNorm(dim)
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self.norm2 = nn.LayerNorm(dim)
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self.norm3 = nn.LayerNorm(dim)
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self.checkpoint = checkpoint
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def forward(self, x, context=None):
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return checkpoint(
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self._forward, (x, context), self.parameters(), self.checkpoint
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)
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def _forward(self, x, context=None):
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x = self.attn1(self.norm1(x)) + x
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x = self.attn2(self.norm2(x), context=context) + x
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x = self.ff(self.norm3(x)) + x
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return x
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class SpatialTransformer(nn.Module):
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"""
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Transformer block for image-like data.
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First, project the input (aka embedding)
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and reshape to b, t, d.
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Then apply standard transformer action.
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Finally, reshape to image
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"""
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def __init__(
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self, in_channels, n_heads, d_head, depth=1, dropout=0.0, context_dim=None
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):
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super().__init__()
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self.in_channels = in_channels
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inner_dim = n_heads * d_head
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self.norm = Normalize(in_channels)
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self.proj_in = nn.Conv2d(
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in_channels, inner_dim, kernel_size=1, stride=1, padding=0
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)
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self.transformer_blocks = nn.ModuleList(
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[
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BasicTransformerBlock(
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inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim
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)
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for d in range(depth)
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]
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)
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self.proj_out = zero_module(
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nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)
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)
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def forward(self, x, context=None):
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# note: if no context is given, cross-attention defaults to self-attention
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b, c, h, w = x.shape
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x_in = x
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x = self.norm(x)
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x = self.proj_in(x)
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x = rearrange(x, "b c h w -> b (h w) c")
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for block in self.transformer_blocks:
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x = block(x, context=context)
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x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w)
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x = self.proj_out(x)
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return x + x_in
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